import pandas as pd
import csv
import plotly.express as px
from plotly.offline import init_notebook_mode

init_notebook_mode()

import pandas as pd
import plotly.express as px

wb_df = pd.read_csv('../data/iunc_whales_only.csv')

custom_order = [
    "Critically Endangered",
    "Endangered",
    "Vulnerable",
    "Near Threatened",
    "Least Concern",
    "Data Deficient"
]

wb_df['redlistCategory'] = pd.Categorical(
    wb_df['redlistCategory'],
    categories=custom_order,
    ordered=True
)

color_map = {
    "Critically Endangered": "#3a5ba0",
    "Endangered": "#3a5ba0",
    "Vulnerable": "#3a5ba0",
    "Near Threatened": "#3a5ba0",
    "Least Concern": "#3a5ba0",
    "Data Deficient": "#999999"
}

fig = px.histogram(
    wb_df,
    x="redlistCategory",
    color="redlistCategory",
    category_orders={"redlistCategory": custom_order},
    labels={
        "redlistCategory": "Classification",
        "count": "Amount"
    },
    color_discrete_map=color_map
)

fig.update_layout(
    title={
        'text': '<b>Endangerment Classifications for Whale Species</b>',
        'x': 0.5,
        'xanchor': 'center',
        'font': {
            'color': '#0a2463',
        }
        },
    height=600,
    xaxis={
        'title': 'Endangerment Classification (Red List Standards)',
        'categoryorder': 'array',
        'categoryarray': custom_order
    },
    yaxis={
        'title': 'Amount of Whale Species'
    },
    margin={'l': 30, 'b': 130, 'r': 30, 't': 30},
    showlegend=False,
)

fig.add_annotation(x=0, y=-0.24,
                   xref="paper", yref="paper",
                   showarrow=False,
                   align='left',
                   xanchor='left', yanchor='bottom',
                   text="Red List Endangerment Classifications for all recorded whale species. <br>" + \
                        'Hover over bars to see specific count of whale species per category.<br>' + \
                        'Species lacking enough data are marked Data Deficient.')
fig.update_annotations(
    font=dict(color='#0a2463')
)

fig.show()

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